Basics: Topic introduction

In this part of the course, we will cover the following concepts:

• Data Science industry overview
• Python as a programming language and the tools used to write and execute Python code
• Basic operations and data types

Module completion checklist

Why are we learning to program?

What level of proficiency do I need?

• To use programming as a tool in your professional toolkit, you don’t need to be a computer scientist or have a similar level of knowledge as one

• The level of proficiency will depend on

  • the problems you are trying to solve on daily basis
  • the subject matter area you are in
  • the level of sophistication of the solutions you would like to implement

• Most of the time, people who are subject matter experts who also use various programming tools and languages are known as data analysts or data scientists

What are the problems you are trying to solve? What is your area of expertise? What level of complexity would you like your programmatic solution to have?

A data scientist can

1. Pose the right question
2. Wrangle the data (gather, clean, and sample data to get a suitable dataset)
3. Manage the data for easy access by the organization
4. Explore the data to generate a hypothesis
5. Make predictions using statistical methods such as regression and classification
6. Communicate the results using visualizations, presentations, and products
   
   

What do data scientists do?

Use programming languages and tools to

1. Wrangle the data (gather, clean, and sample data to get a suitable dataset)
2. Manage the data for easy access by the organization
3. Explore the data to generate a hypothesis
4. Make predictions using statistical methods such as regression and classification

Stemming from the list above, the programming skills should cover knowing a programming language (or two, or three, or …) to a degree that allows you to perform these operations!

Module completion checklist

Data science control cycle: framework for data

• There is a protocol or standard for working with data that most data scientists follow
• The cycle involves everything from asking the right questions and being knowledgeable about the data you’re studying, to optimizing your model’s performance

Data Science Control Cycle (DSCC)

Question - Which part of the cycle do you think takes up the most time?

1. Data Cleaning and Collection
2. Data Analyzing and Modelling
3. Learning New Techniques

How you think data scientists spend their time?

How data scientists actually spend their time

DSCC: SMART questions

DSCC: research

DSCC: modeling

DSCC: steps 4 - 5

DSCC: step 6

Predictive modeling

• What is a predictive model and why would we use one?

  • Predictive Analytics is a mathematical formula of past data to predict the outcomes in the future
  • Predictive modeling allows us to do more with less
  • Predictive modeling allows us to identify individuals who are most likely to take or not take an action

Example: campaign response rate

Predictive Modeling: When is it not useful?

Predictive modeling: High Level Overview

Module completion checklist

What is Python?

What can you do with Python?

Knowledge check

Link: kc params$basics_knowledge_check_1

Module completion checklist

Congratulations on completing this module!